• 한국농공학회지
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• 제35권3호
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• pp.23-35
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• 1993

The objectives of this paper were to validate the proposed network flow model using field data and to demonstrate the model applicability for various purposes. The model was tested with data from the Banweol watershed, where an intentive streamflow gauging system has been established. Model parameters were not calibrated with field data so that it can be validated as ungaged conditions. Three different schemes were employed to represent the drainage system of the tested watershed : a single, complex, and detailed network. The single network assumed the watershed as a cell, while complex and detailed networks considered several cells. The results from different schemes were individually compared satisfactorily to the observed daily stages at the Banweol reservoir located at the outlet of the watershed. The results from three schemes were in close agreement with each other, Justifying that the model performs very well for different network schemes being used. Daily streamflow from three network schemes was compared for a selected reach within the watershed. The results were very close to each other regardless of network formulation. And the model was applied to simulate daily streamflow before and after the construction of a reservoir at a reach. The differences were discussed, which reflected the influences of the dam construction upon the downstream hydrology. Similar appliocations may be possible to identify the effects of hydraulic structures on streamflow.

• 상하수도학회지
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• 제29권3호
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• pp.427-436
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• 2015

Developing two process models to simulate wastewater treatment process is needed to draw a comparison between measured BOD data and estimated process model data: a mathematical model based on the process mass-balance and an ANN (artificial neural network) model. Those two types of simulator can fit well in terms of effluent BOD data, which models are formulated based on the distinctive five parameters: influent flow rate, effluent flow rate, influent BOD concentration, biomass concentration, and returned sludge percentage. The structuralized mass-balance model and ANN modeI with seasonal periods can estimate data set more precisely, and changing optimization algorithm for the penalty could be a useful option to tune up the process behavior estimations. An complex model such as ANN model coupled with mass-balance equation will be required to simulate process dynamics more accurately.

• 설비공학논문집
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• 제24권6호
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• pp.538-544
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• 2012

Analysis of thermal performance is required for the economic operation of turbine cycle of power plant. We developed corrective model of main feed water flow which is the most important parameter for the precise analysis of turbine cycle performance. Classification model for the identification of feed water flow measurement status was applied to increase the suitability of the corrective model. We used neural network and support vector machine to develop estimation model of main feed water flow with more generalization capability. The estimation model can be used practically to evaluate corrective performance of turbine cycle plant.

• Computers and Concrete
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• 제5권5호
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• 대한기계학회논문집A
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• 제20권5호
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• pp.1426-1435
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• 1996

In this paper. a network theory for generaltransmission systme was extended considering the direction of power flow. Also, a modified network model was suggested for a node with 4 shafts in order to verify the power flow. Based on the extended network theory, a simulation program was developed to analyze a hydro-mecaanical tranmission(HMT) system consistion of two hydrostatic pump motors, severeal planetary gear trains steer differential gear. The simulation result showed that the extendednotwork analysis program develped can predict the power circulation as well as the magnitude of torque and speed for each transmission element and can be used design tool for genaral power transmission system.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.88-97
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• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2001년도 추계학술발표회
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• pp.27-30
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• 2001

Flow and transport at fracture intersections, and their effects on network scale transport, are investigated in three-dimensional random fracture networks. Fracture intersection mixing rules complete mixing and streamline routing are defined in terms of fluxes normal to the intersection line between two fractures. By analyzing flow statistics and particle transfer probabilities distributed along fracture intersections, it is shown that for various network structures with power law size distributions of fractures, the choice of intersection mixing rule makes comparatively little difference in the overall simulated solute migration patterns. The occurrence and effects of local flows around an intersection (local flow cells) are emphasized. Transport simulations at fracture intersections indicate that local flow circulations can arise from variability within the hydraulic head distribution along intersections, and from the internal no flow condition along fracture boundaries. These local flow cells act as an effective mechanism to enhance the nondiffusive breakthrough tailing often observed in discrete fracture networks. It is shown that such non-Fickian (anomalous) solute transport can be accounted for by considering only advective transport, in the framework of a continuous time random walk model. To clarify the effect of forest environmental changes (forest type difference and clearcut) on water storage capacity in soil and stream flow, watershed had been investigated.

• ETRI Journal
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• 제30권2호
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• pp.205-215
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• 2008

Many control schemes have been proposed for flow-level traffic control. However, flow-level traffic control is implemented only in limited areas such as traffic monitoring and traffic control at edge nodes. No clear solution for end-to-end architecture has been proposed. Scalability and the lack of a business model are major problems for deploying end-to-end flow-level control architecture. This paper introduces an end-to-end transport architecture and a scalable control mechanism to support the various flow-level QoS requests from applications.

• 한국수자원학회논문집
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• 제41권11호
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• pp.1123-1133
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• 2008

유역의 강우-유출 현상을 분포형으로 모의하기 위해서는 삼각형 혹은 사각형 요소로 유역을 모형화하고 각 요소에서의 수문성분의 변화를 해석하여야 한다. 본 연구는 사각형 요소인 격자로 모형화된 유역에서의 강우-유출 현상을 1차원 운동파 방정식을 이용하여 모의할 때 각 격자에서 발생된 흐름의 추적을 위한 격자 네트워크 해석에 대해 수행하였다. 격자의 흐름방향은 D8-method(deterministic eight-neighbors method)에 의해 결정된 단방향 흐름정보를 이용하였고, 각 격자별 흐름방향과 흐름누적수 정보를 이용하여 해당 격자의 계산 순서를 결정하게 된다. 또한 1차원 운동파 방정식을 유한체적법으로 해석할 때 격자간의 흐름방향 형태에 따른 해석방법을 제시하고, 이를 격자별 유출량 계산에 적용하였다. 본 연구에서 제시된 격자 네트워크 해석법은 물리적 기반의 분포형 강우-유출 모형인 GRM(Grid based Rainfall-runoff Model)에 적용하였으며, 단순화된 가상의 유역에 대한 모의결과를 $Vflo^{TM}$ 모형의 모의결과와 비교함으로써 타당성을 검토하였다. 또한 한강 수계의 중랑천 유역의 적용을 통해 실유역에 대한 적용성을 검토하였다. 중랑천 유역의 적용결과 모의된 유출 수문곡선은 관측 수문곡선을 잘 재현하였으며, 이에 따라 격자 네트워크 해석 과정의 실유역 적용이 타당한 것으로 나타났다.

• 상하수도학회지
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• 제21권3호
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model